Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11 ah MAC Layer
The IEEE 802.11 ah standard is introduced to address the growing scale of internet of things
(IoT) applications. To reduce contention and enhance energy efficiency in the system, the …
(IoT) applications. To reduce contention and enhance energy efficiency in the system, the …
A deep Q-network approach to optimize spatial reuse in WiFi networks
The proliferation of IEEE 802.11 or WiFi networks, and the explosive growth in traffic
demands call for solutions to maximize the capacity of WiFi networks. Hence, maximizing the …
demands call for solutions to maximize the capacity of WiFi networks. Hence, maximizing the …
QoS-oriented media access control using reinforcement learning for next-generation WLANs
J Lei, L Li, Y Wang - Computer Networks, 2022 - Elsevier
Orthogonal frequency division multiple access (OFDMA) is introduced in IEEE 802.11 ax to
satisfy massive transmission demands. However, the uplink OFDMA-based random access …
satisfy massive transmission demands. However, the uplink OFDMA-based random access …
Distributed convolutional deep reinforcement learning based OFDMA MAC for 802.11 ax
D Kotagiri, K Nihei, T Li - ICC 2021-IEEE International …, 2021 - ieeexplore.ieee.org
The IEEE 802.11 ax also known as Wi-Fi 6, incorporates multi-user (MU) Orthogonal
Frequency Division Multiple Access (OFDMA) based distributed up-link communication, in …
Frequency Division Multiple Access (OFDMA) based distributed up-link communication, in …
Cfx: contention-free channel access for IEEE 802.11 ax
K Lee, D Kim - Sensors, 2022 - mdpi.com
Orthogonal frequency-division multiple access (OFDMA) has attracted great attention as a
key technology for uplink enhancement for Wi-Fi, since it can effectively reduce network …
key technology for uplink enhancement for Wi-Fi, since it can effectively reduce network …
Sequential state q-learning uplink resource allocation in multi-ap 802.11 be network
Y Liu, Y Yu, Z Du, L Cuthbert - 2022 IEEE 96th Vehicular …, 2022 - ieeexplore.ieee.org
Expected high demand of user applications in the WLAN is a driver for WLANs to share
radio resources more efficiently. The move to 802.11 be with OFDMA and MU-MIMO makes …
radio resources more efficiently. The move to 802.11 be with OFDMA and MU-MIMO makes …
A Hierarchical Deep Learning Approach for Optimizing CCA Threshold and Transmit Power in Wi-Fi Networks
The nodes, eg, access points and clients, in current WiFi networks rely on carrier sense
multiple access (CSMA) for channel access. This means they rely on a clear channel …
multiple access (CSMA) for channel access. This means they rely on a clear channel …
Learning-aided client association control for high-density WLANs
As wireless local area network (WLAN) continues to become popular, there is an increasing
number of clients with huge data traffic demands. Especially, some high client-density …
number of clients with huge data traffic demands. Especially, some high client-density …
Application of deep neural network and deep reinforcement learning in wireless communication
M Li, H Li - Plos one, 2020 - journals.plos.org
Objective To explore the application of deep neural networks (DNNs) and deep
reinforcement learning (DRL) in wireless communication and accelerate the development of …
reinforcement learning (DRL) in wireless communication and accelerate the development of …
Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …